OpenRaiser/Intern-Atlas huggingface dataset:
Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D Rigid-Body Motion Registration
Venue: arXiv cs.CV 2011
Year: 2011

Motivated by an emerging theory of robust low-rank matrix representation, in this paper, we introduce a novel solution for online rigid-body motion registration. The goal is to develop algorithmic techniques that enable a robust, real-time motion registration solution suitable for low-cost, portable 3-D camera devices. Assuming 3-D image features are tracked via a standard tracker, the algorithm first utilizes Robust PCA to initialize a low-rank shape representation of the rigid body. Robust PCA finds the global optimal solution of the initialization, while its complexity is comparable to singular value decomposition. In the online update stage, we propose a more efficient algorithm for sparse subspace projection to sequentially project new feature observations onto the shape subspace. The lightweight update stage guarantees the real-time performance of the solution while maintaining good registration even when the image sequence is contaminated by noise, gross data corruption, outlying features, and missing data. The state-of-the-art accuracy of the solution is validated through extensive simulation and a real-world experiment, while the system enjoys one to two orders of magnitude speed-up compared to well-established RANSAC solutions. The new algorithm will be released online to aid peer evaluation.
Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition
Venue: arXiv cs.MM 2011
Year: 2011

This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of midlevel features, we develop a spectral embedding approach to latent semantic learning based on L1-graph, without the need to tune any parameter for graph construction as a key step of manifold learning. More importantly, we construct the L1-graph with structured sparse representation, which can be obtained by structured sparse coding with its structured sparsity ensured by novel L1-norm hypergraph regularization over mid-level features. In the new embedding space, we learn latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for human action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our latent semantic learning method can explore the manifold structure of mid-level features in both L1-graph construction and spectral embedding, which results in compact but discriminative high-level features. The experimental results on the commonly used KTH action dataset and unconstrained YouTube action dataset show the superior performance of our method.
An Application Driven Analysis of the ParalleX Execution Model
Venue: arXiv cs.DC 2011
Year: 2011

Exascale systems, expected to emerge by the end of the next decade, will require the exploitation of billion-way parallelism at multiple hierarchical levels in order to achieve the desired sustained performance. The task of assessing future machine performance is approached by identifying the factors which currently challenge the scalability of parallel applications. It is suggested that the root cause of these challenges is the incoherent coupling between the current enabling technologies, such as Non-Uniform Memory Access of present multicore nodes equipped with optional hardware accelerators and the decades older execution model, i.e., the Communicating Sequential Processes (CSP) model best exemplified by the message passing interface (MPI) application programming interface. A new execution model, ParalleX, is introduced as an alternative to the CSP model. In this paper, an overview of the ParalleX execution model is presented along with details about a ParalleX-compliant runtime system implementation called High Performance ParalleX (HPX). Scaling and performance results for an adaptive mesh refinement numerical relativity application developed using HPX are discussed. The performance results of this HPX-based application are compared with a counterpart MPI-based mesh refinement code. The overheads associated with HPX are explored and hardware solutions are introduced for accelerating the runtime system.
A Market for Air Traffic Flow Management
Venue: arXiv cs.CC 2011
Year: 2011

The two somewhat conflicting requirements of efficiency and fairness make ATFM an unsatisfactorily solved problem, despite its overwhelming importance. In this paper, we present an economics motivated solution that is based on the notion of a free market. Our contention is that in fact the airlines themselves are the best judge of how to achieve efficiency and our market-based solution gives them the ability to pay, at the going rate, to buy away the desired amount of delay on a per flight basis. The issue of fairness is simply finessed away by our solution -- whoever pays gets smaller delays. We show how our solution has the potential of enabling travelers from a large spectrum of affordability and punctuality requirements to achieve an end that is most desirable to them. Our market model is particularly simple, requiring only one parameter per flight from the airline company. Furthermore, we show that it admits a combinatorial, strongly polynomial algorithm for computing an equilibrium landing schedule and prices.
Complexity of Model Checking Recursion Schemes for Fragments of the Modal Mu-Calculus
Venue: arXiv cs.LO 2011
Year: 2011

Ong has shown that the modal mu-calculus model checking problem (equivalently, the alternating parity tree automaton (APT) acceptance problem) of possibly-infinite ranked trees generated by order-n recursion schemes is n-EXPTIME complete. We consider two subclasses of APT and investigate the complexity of the respective acceptance problems. The main results are that, for APT with a single priority, the problem is still n-EXPTIME complete; whereas, for APT with a disjunctive transition function, the problem is (n-1)-EXPTIME complete. This study was motivated by Kobayashi's recent work showing that the resource usage verification of functional programs can be reduced to the model checking of recursion schemes. As an application, we show that the resource usage verification problem is (n-1)-EXPTIME complete.
Two-hop Communication with Energy Harvesting
Venue: arXiv cs.IT 2011
Year: 2011

Communication nodes with the ability to harvest energy from the environment have the potential to operate beyond the timeframe limited by the finite capacity of their batteries; and accordingly, to extend the overall network lifetime. However, the optimization of the communication system in the presence of energy harvesting devices requires a new paradigm in terms of power allocation since the energy becomes available over time. In this paper, we consider the problem of two-hop relaying in the presence of energy harvesting nodes. We identify the optimal offline transmission scheme for energy harvesting source and relay when the relay operates in the full-duplex mode. In the case of a half-duplex relay, we provide the optimal transmission scheme when the source has a single energy packet.
On the relationship between classes P and NP
Venue: arXiv cs.DM 2011
Year: 2011

In this paper we discusses the relationship between the known classes P and NP. We show that the difficulties in solving problem "P versus NP" have methodological in nature. An algorithm for solving any problem is sensitive to even small changes in its formulation. As we will shown in the paper, these difficulties are exactly in the formulation of some problems of the class NP.
Energy-Aware Load Balancing in Content Delivery Networks
Venue: arXiv cs.NI 2011
Year: 2011

Internet-scale distributed systems such as content delivery networks (CDNs) operate hundreds of thousands of servers deployed in thousands of data center locations around the globe. Since the energy costs of operating such a large IT infrastructure are a significant fraction of the total operating costs, we argue for redesigning CDNs to incorporate energy optimizations as a first-order principle. We propose techniques to turn off CDN servers during periods of low load while seeking to balance three key design goals: maximize energy reduction, minimize the impact on client-perceived service availability (SLAs), and limit the frequency of on-off server transitions to reduce wear-and-tear and its impact on hardware reliability. We propose an optimal offline algorithm and an online algorithm to extract energy savings both at the level of local load balancing within a data center and global load balancing across data centers. We evaluate our algorithms using real production workload traces from a large commercial CDN. Our results show that it is possible to reduce the energy consumption of a CDN by more than 55% while ensuring a high level of availability that meets customer SLA requirements and incurring an average of one on-off transition per server per day. Further, we show that keeping even 10% of the servers as hot spares helps absorb load spikes due to global flash crowds with little impact on availability SLAs. Finally, we show that redistributing load across proximal data centers can enhance service availability significantly, but has only a modest impact on energy savings.
Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems
Venue: arXiv cs.MA 2011
Year: 2011

Effective coordination of agents actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littmans Q-routing and Stones TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.
An Improved Search Algorithm for Optimal Multiple-Sequence Alignment
Venue: arXiv cs.AI 2011
Year: 2011

Multiple sequence alignment (MSA) is a ubiquitous problem in computational biology. Although it is NP-hard to find an optimal solution for an arbitrary number of sequences, due to the importance of this problem researchers are trying to push the limits of exact algorithms further. Since MSA can be cast as a classical path finding problem, it is attracting a growing number of AI researchers interested in heuristic search algorithms as a challenge with actual practical relevance. In this paper, we first review two previous, complementary lines of research. Based on Hirschbergs algorithm, Dynamic Programming needs O(kN^(k-1)) space to store both the search frontier and the nodes needed to reconstruct the solution path, for k sequences of length N. Best first search, on the other hand, has the advantage of bounding the search space that has to be explored using a heuristic. However, it is necessary to maintain all explored nodes up to the final solution in order to prevent the search from re-expanding them at higher cost. Earlier approaches to reduce the Closed list are either incompatible with pruning methods for the Open list, or must retain at least the boundary of the Closed list. In this article, we present an algorithm that attempts at combining the respective advantages; like A* it uses a heuristic for pruning the search space, but reduces both the maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The underlying idea is to conduct a series of searches with successively increasing upper bounds, but using the DP ordering as the key for the Open priority queue. With a suitable choice of thresholds, in practice, a running time below four times that of A* can be expected. In our experiments we show that our algorithm outperforms one of the currently most successful algorithms for optimal multiple sequence alignments, Partial Expansion A*, both in time and memory. Moreover, we apply a refined heuristic based on optimal alignments not only of pairs of sequences, but of larger subsets. This idea is not new; however, to make it practically relevant we show that it is equally important to bound the heuristic computation appropriately, or the overhead can obliterate any possible gain. Furthermore, we discuss a number of improvements in time and space efficiency with regard to practical implementations. Our algorithm, used in conjunction with higher-dimensional heuristics, is able to calculate for the first time the optimal alignment for almost all of the problems in Reference 1 of the benchmark database BAliBASE.
Generative Prior Knowledge for Discriminative Classification
Venue: arXiv cs.AI 2011
Year: 2011

We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of newsgroup categorization. WordNet is viewed as an approximate, but readily available source of background knowledge, and our framework is capable of utilizing it in a flexible way.
The Statistical Coherence-based Theory of Robust Recovery of Sparsest Overcomplete Representation
Venue: arXiv cs.IT 2011
Year: 2011

The recovery of sparsest overcomplete representation has recently attracted intensive research activities owe to its important potential in the many applied fields such as signal processing, medical imaging, communication, and so on. This problem can be stated in the following, i.e., to seek for the sparse coefficient vector of the given noisy observation over a redundant dictionary such that, where is the corrupted error. Elad et al. made the worst-case result, which shows the condition of stable recovery of sparest overcomplete representation over is where . Although it's of easy operation for any given matrix, this result can't provide us realistic guide in many cases. On the other hand, most of popular analysis on the sparse reconstruction relies heavily on the so-called RIP (Restricted Isometric Property) for matrices developed by Candes et al., which is usually very difficult or impossible to be justified for a given measurement matrix. In this article, we introduced a simple and efficient way of determining the ability of given D used to recover the sparse signal based on the statistical analysis of coherence coefficients, where is the coherence coefficients between any two different columns of given measurement matrix . The key mechanism behind proposed paradigm is the analysis of statistical distribution (the mean and covariance) of . We proved that if the resulting mean of are zero, and their covariance are as small as possible, one can faithfully recover approximately sparse signals from a minimal number of noisy measurements with overwhelming probability. The resulting theory is not only suitable for almost all models - e.g. Gaussian, frequency measurements-discussed in the literature of compressed sampling, but also provides a framework for new measurement strategies as well.
On automatic infinite permutations
Venue: arXiv cs.DM 2011
Year: 2011

An infinite permutation $α$ is a linear ordering of $\mathbb N$. We study properties of infinite permutations analogous to those of infinite words, and show some resemblances and some differences between permutations and words. In this paper, we try to extend to permutations the notion of automaticity. As we shall show, the standard definitions which are equivalent in the case of words are not equivalent in the context of permutations. We investigate the relationships between these definitions and prove that they constitute a chain of inclusions. We also construct and study an automaton generating the Thue-Morse permutation.
The Pollution Effect: Optimizing Keyword Auctions by Favoring Relevant Advertising
Venue: arXiv cs.GT 2011
Year: 2011

Most search engines sell slots to place advertisements on the search results page through keyword auctions. Advertisers offer bids for how much they are willing to pay when someone enters a search query, sees the search results, and then clicks on one of their ads. Search engines typically order the advertisements for a query by a combination of the bids and expected clickthrough rates for each advertisement. In this paper, we extend a model of Yahoo's and Google's advertising auctions to include an effect where repeatedly showing less relevant ads has a persistent impact on all advertising on the search engine, an impact we designate as the pollution effect. In Monte-Carlo simulations using distributions fitted to Yahoo data, we show that a modest pollution effect is sufficient to dramatically change the advertising rank order that yields the optimal advertising revenue for a search engine. In addition, if a pollution effect exists, it is possible to maximize revenue while also increasing advertiser, and publisher utility. Our results suggest that search engines could benefit from making relevant advertisements less expensive and irrelevant advertisements more costly for advertisers than is the current practice.
Lattices for Physical-layer Secrecy: A Computational Perspective
Venue: arXiv cs.IT 2011
Year: 2011

In this paper, we use the hardness of quantization over general lattices as the basis of developing a physical layer secrecy system. Assuming that the channel state observed by the legitimate receiver and the eavesdropper are distinct, this asymmetry is used to develop a cryptosystem that resembles the McEliece cryptosystem, designed to be implemented at the physical layer. We ensure that the legitimate receiver observes a specific lattice over which decoding is known to be possible in polynomial-time. while the eavesdropper observes a lattice over which decoding will prove to have the complexity of lattice quantization over a general lattice
Low-rank data modeling via the Minimum Description Length principle
Venue: arXiv cs.IT 2011
Year: 2011

Robust low-rank matrix estimation is a topic of increasing interest, with promising applications in a variety of fields, from computer vision to data mining and recommender systems. Recent theoretical results establish the ability of such data models to recover the true underlying low-rank matrix when a large portion of the measured matrix is either missing or arbitrarily corrupted. However, if low rank is not a hypothesis about the true nature of the data, but a device for extracting regularity from it, no current guidelines exist for choosing the rank of the estimated matrix. In this work we address this problem by means of the Minimum Description Length (MDL) principle -- a well established information-theoretic approach to statistical inference -- as a guideline for selecting a model for the data at hand. We demonstrate the practical usefulness of our formal approach with results for complex background extraction in video sequences.
An Entropy Based Method for Local Time-Adaptation of the Spectrogram
Venue: arXiv cs.SD 2011
Year: 2011

We propose a method for automatic local time-adaptation of the spectrogram of audio signals: it is based on the decomposition of a signal within a Gabor multi-frame through the STFT operator. The sparsity of the analysis in every individual frame of the multi-frame is evaluated through the Rényi entropy measures: the best local resolution is determined minimizing the entropy values. The overall spectrogram of the signal we obtain thus provides local optimal resolution adaptively evolving over time. We give examples of the performance of our algorithm with an instrumental sound and a synthetic one, showing the improvement in spectrogram displaying obtained with an automatic adaptation of the resolution. The analysis operator is invertible, thus leading to a perfect reconstruction of the original signal through the analysis coefficients.
Negotiating Socially Optimal Allocations of Resources
Venue: arXiv cs.MA 2011
Year: 2011

A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory to assess the social welfare of such an agent society. In this paper, we study an abstract negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then analyse how these deals affect social welfare for different instances of the basic framework and different interpretations of the concept of social welfare itself. In particular, we show how certain classes of deals are both sufficient and necessary to guarantee that a socially optimal allocation of resources will be reached eventually.
Performance of Orthogonal Matching Pursuit for Multiple Measurement Vectors
Venue: arXiv cs.IT 2011
Year: 2011

In this paper, we consider orthogonal matching pursuit (OMP) algorithm for multiple measurement vectors (MMV) problem. The robustness of OMPMMV is studied under general perturbations---when the measurement vectors as well as the sensing matrix are incorporated with additive noise. The main result shows that although exact recovery of the sparse solutions is unrealistic in noisy scenario, recovery of the support set of the solutions is guaranteed under suitable conditions. Specifically, a sufficient condition is derived that guarantees exact recovery of the sparse solutions in noiseless scenario.
ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons
Venue: arXiv cs.CV 2011
Year: 2011

This paper describes an intelligent system ABHIVYAKTI, which would be pervasive in nature and based on the Computer Vision. It would be very easy in use and deployment. Elder and sick people who are not able to talk or walk, they are dependent on other human beings and need continuous monitoring, while our system provides flexibility to the sick or elder person to announce his or her need to their caretaker by just showing a particular gesture with the developed system, if the caretaker is not nearby. This system will use fingertip detection techniques for acquiring gesture and Artificial Neural Networks (ANNs) will be used for gesture recognition.